Statistics 154

Modern Statistical Prediction and Machine Learning

Spring 2022



Instructor: Nusrat Rabbee,

Office: 305 Evans.

Office hours: F 2:00-4:00pm (zoom), or after class or by appointment


Graduate Student Instructor: Austin Zane,

Office hours:  W 2-4p (428 Evans/zoom) ; Th 3-5p (428 Evans)


Website: We will post announcements, assignments, lecture notes etc. on Check regularly for updates.


Schedule: There will be lectures two days a week, TTh 5:00-6:30, in Etcheverry 3108. There will also be weekly sections, scheduled M 9-11p or 3-5p, starting 1/24. Attendance to both lectures and sections is highly encouraged.



1.   Required: James, Witten, Hastie, Tibshirani. An Introduction to Statistical Learning.  Hardcopy. Online version. (Courtesy of the authors)

2.   Optional: Hastie, Tibshirani and Friedman. The Elements of Statistical Learning. Second Edition. This book is more mathematically advanced than the one above. Hardcopy. Online version. (Courtesy of the authors). This text will not be used directly for this course and is a reference for more theoretical details.

3.   Optional: Rabbee, N. Biomarker analysis in Clinical Trials Using R. This book is mathematically suited for advanced studies than the first one above.  It may be used as a reference for graphical models for visualization methods. Copies of relevant chapters will be provided (courtesy of author).


Exams and grading: There will be two written exams (Th during class or take home) and a final project (due M 05/09 @noon). There will be three to four quizzes during section. There will be no make-up quiz, written exam or final project due date adjustments; do not take the class if you are not available at these dates and times. Your grade will be 30% best three quizzes, 30% written exams, 40% final project.


Assignments: There will be six to seven assignments. They are announced in bCourses on Fridays. The assignments are not to be handed in. You should do the assignments in teams or by yourself. The quizzes will be similar to the assignment sets.


Academic Integrity: You may collaborate with your assigned team for the final project and you will share the same score. No collaboration is allowed in the quizzes or exams. Penalties for cheating will be severe. Here are more details. Special instructions for final project teams will be announced later.


Communicating: Questions about lectures should be directed primarily to me after lecture or in office hours, about section and assignments primarily to the GSI. Emails are generally discouraged. Write to me only if you have any pressing administrative issues. Emails should be brief, marked “stat 154” in the subject and crisp for a good chance at being answered. Regardless, you are encouraged to come to any of our office hours or stay after class: talking is usually more effective than sending email. Feedback is always welcome.


Support: If you experience stress or challenges, depression or anxiety, you are strongly encouraged to seek help. Please ask department staff, faculty or a trusted family member - for support sooner rather than later in the semester. We are here to help you get connected to the support you need.


Prerequisites: Mathematics 53 and 54 or equivalents; 110 is highly recommended. Statistics 135 or equivalent. Statistics 133 preferred. Stat 151A is recommended. Scripting language and R experience required. Mathematics 55 or equivalent exposure to counting arguments is recommended but not required.